Using pretrained models from sparknlp on Databricks - pyspark

Question

I am trying to follow the official examples from John Snow Labs but every time I get a TypeError: 'JavaPackage' object is not callable error. I followed all of the steps in the Databricks install documentation but no matter what walkthrough I try, either this one or this one it fails.
An example of the first (after doing the installs):
import sparknlp
from sparknlp.pretrained import *
pipeline = PretrainedPipeline('recognize_entities_dl', 'en')
recognize_entities_dl download started this may take some time.
TypeError: 'JavaPackage' object is not callable
TypeError Traceback (most recent call last)
<command-937510457011238> in <module>
----> 1 pipeline = PretrainedPipeline('recognize_entities_dl', 'en')
2
3 # ner_bert = NerDLModel.pretrained('ner_dl_bert')
4
5 # pipeline = PretrainedPipeline('recognize_entities_dl', 'en', 'https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ner_dl_bert_en_2.4.3_2.4_1584624951079.zip')
/databricks/python/lib/python3.7/site-packages/sparknlp/pretrained.py in __init__(self, name, lang, remote_loc, parse_embeddings, disk_location)
89 def __init__(self, name, lang='en', remote_loc=None, parse_embeddings=False, disk_location=None):
90 if not disk_location:
---> 91 self.model = ResourceDownloader().downloadPipeline(name, lang, remote_loc)
92 else:
93 self.model = PipelineModel.load(disk_location)
/databricks/python/lib/python3.7/site-packages/sparknlp/pretrained.py in downloadPipeline(name, language, remote_loc)
49 def downloadPipeline(name, language, remote_loc=None):
50 print(name + " download started this may take some time.")
---> 51 file_size = _internal._GetResourceSize(name, language, remote_loc).apply()
52 if file_size == "-1":
53 print("Can not find the model to download please check the name!")
/databricks/python/lib/python3.7/site-packages/sparknlp/internal.py in __init__(self, name, language, remote_loc)
190 def __init__(self, name, language, remote_loc):
191 super(_GetResourceSize, self).__init__(
--> 192 "com.johnsnowlabs.nlp.pretrained.PythonResourceDownloader.getDownloadSize", name, language, remote_loc)
193
194
/databricks/python/lib/python3.7/site-packages/sparknlp/internal.py in __init__(self, java_obj, *args)
127 super(ExtendedJavaWrapper, self).__init__(java_obj)
128 self.sc = SparkContext._active_spark_context
--> 129 self._java_obj = self.new_java_obj(java_obj, *args)
130 self.java_obj = self._java_obj
131
/databricks/python/lib/python3.7/site-packages/sparknlp/internal.py in new_java_obj(self, java_class, *args)
137
138 def new_java_obj(self, java_class, *args):
--> 139 return self._new_java_obj(java_class, *args)
140
141 def new_java_array(self, pylist, java_class):
/databricks/spark/python/pyspark/ml/wrapper.py in _new_java_obj(java_class, *args)
65 java_obj = getattr(java_obj, name)
66 java_args = [_py2java(sc, arg) for arg in args]
---> 67 return java_obj(*java_args)
68
69 #staticmethod
TypeError: 'JavaPackage' object is not callable
I get a similar if not the exact error if I try:
pipeline = PretrainedPipeline('recognize_entities_dl', 'en', 'https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/ner_dl_bert_en_2.4.3_2.4_1584624951079.zip')
I also get the same error for the second example. The Databricks Runtime Version is: 6.5 (includes Apache Spark 2.4.5, Scala 2.11), which is on the list of approved runtimes.
I'm not sure what the error messages mean or how to resolve them.

Answer 1

I found out that 'JavaPackage' object is not callable is caused by the spark-nlp (assembly jars) missing. So I made sure that these jars were downloaded and then placed in BOTH the executor and driver. E.g
when building the Spark docker image do something like
RUN cd /opt/spark/jars && \
wget https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/spark-nlp-assembly-2.6.4.jar
and also on the driver image/machine make sure the jar exists in the local directoy. Then set
conf.set("spark.driver.extraClassPath", "/opt/spark/jars/spark-nlp-assembly-2.6.4.jar")
conf.set("spark.executor.extraClassPath", "/opt/spark/jars/spark-nlp-assembly-2.6.4.jar")
The solution for databricks might be a bit different so instead of baking in the jars you may need to host them on S3 and refer to them that way.